Everything there is to Know about Stochastically Known Logs
Avigdor Gal
When it comes to data, the world has seen a tremendous change from the days back then when data was neatly stored in well-organized, centrally managed, carefully monitored organizational information systems. Data nowadays can be found in a haphazard collection of datasets, some of which are collected from sensors (humans and artificial) while others are AI generated. Data is available anywhere and everywhere, and its quality is by no means guaranteed or monitored. Process data is no different. What used to be a collection of well-designed protocols, accompanied by traces that record processes in a clear and clean manner, now ranges from noisy labeled data, to partially known processes, through machine tagged activities. The starting point of this talk is the cornerstone of process mining, namely the process data log. We shall inspect the log through the lens of uncertainty, motivating the need for stochastically known logs through modern process mining applications. We shall also investigate the relationships between stochastically known logs and models of probabilistic databases. Then, we will dive into the impact of stochastically known logs on various tasks of process mining. We shall conclude with a discussion of challenges we face as a community when transiting from deterministic logs to stochastically known logs.
Avigdor Gal
Technion – Israel Institute of Technology
Avigdor Gal is the Benjamin and Florence Free Chaired Professor of Data Science and the Co-chair of the Center for Humanities & AI at the Technion – Israel Institute of Technology. He is with the Faculty of Data & Decision Sciences, where he led the design of the first engineering program in data science in Israel (and possibly the world). Gal’s research focuses on elements of data integration and process management and mining under uncertainty, making use of state-of-the-art machine learning and deep learning techniques to offer an improved data quality with close to 200 publications in leading journals, books, and conference proceedings (including multiple best paper and test-of-time awards). His research is implemented, through his ties as a consultant, in multiple industries including FinTech. Gal actively pursues projects that involve real-world applications. He was recently a member of a MAGNET project (Israeli Innovation Authority) FoodIoT, where transfer of knowledge from academia to the food industry (including the biggest food companies in Israel) assists adopting modern data science techniques to improve their performance. Before that, Gal was involved in multiple European projects in diverse application areas such as smart cities and medicine. In recent years, with the increasing penetration of AI to all aspects of life, Gal has been involved in developing methods for embedding responsible AI in companies and government authorities through an education process that increases dialogue abilities between data scientists and other stake-holders (e.g., lawyers and regulators).
